02417nas a2200193   4500008004100000245009500041210006900136520178100205653001401986653001602000653002202016653002802038653001902066653002402085100001702109700001702126700001802143856006202161       2019                        eng d00aNonintrusive methods for biomass estimation in aquaculture with emphasis on fish: a review0 aNonintrusive methods for biomass estimation in aquaculture with 3 a<p>Fish biomass estimation is one of the most common and important practices in aquaculture. The regular acquisition of fish biomass information has been identi- fied as an urgent need for managers to optimize daily feeding, control stocking densities and ultimately determine the optimal time for harvesting. However, it is difficult to estimate fish biomass without human intervention because fishes are sensitive and move freely in an environment where visibility, lighting and stability are uncontrollable. Until now, fish biomass estimation has been mostly based on manual sampling, which is usually invasive, time-consuming and laborious. Therefore, it is imperative and highly desirable to develop a noninvasive, rapid and cost-effective means. Machine vision, acoustics, environmental DNA and resistivity counter provide the possibility of developing nonintrusive, faster and cheaper methods for in situ estimation of fish biomass. This article summarizes the development of these nonintrusive methods for fish biomass estimation over the past three decades and presents their basic concepts and principles. The strengths and weaknesses of each method are analysed and future research direc- tions are also presented. Studies show that the applications of information tech- nology such as advanced sensors and communication technologies have great significance to accelerate the development of new means and techniques for more effective biomass estimation. However, the accuracy and intelligence still need to be improved to meet intensive aquaculture requirements. Through close coopera- tion between fisheries experts and engineers, the precision and the level of intelli- gence for fish biomass estimation will be further improved based on the above methods.</p>
10aacoustics10aaquaculture10aenvironmental DNA10afish biomass estimation10amachine vision10aresistivity counter1 aLi, Daoliang1 aHao, Yinfeng1 aDuan, Yanqing  uhttps://onlinelibrary.wiley.com/doi/abs/10.1111/raq.12388